Saving best model of SciBERT_AsymmetricLoss_25K_bs64_P4_N1 to hub

README.md

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+---
+base_model: allenai/scibert_scivocab_uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: SciBERT_AsymmetricLoss_25K_bs64_P4_N1
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# SciBERT_AsymmetricLoss_25K_bs64_P4_N1
+
+This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 30.2502
+- Accuracy: 0.9871
+- Precision: 0.4247
+- Recall: 0.8998
+- F1: 0.5770
+- Hamming: 0.0129
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 25000
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Hamming |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
+| 36.6287       | 0.16  | 5000  | 34.9978         | 0.9852   | 0.3863    | 0.8728 | 0.5355 | 0.0148  |
+| 33.8929       | 0.32  | 10000 | 32.4942         | 0.9857   | 0.3958    | 0.8901 | 0.5480 | 0.0143  |
+| 32.5419       | 0.47  | 15000 | 31.3170         | 0.9867   | 0.4162    | 0.8941 | 0.5680 | 0.0133  |
+| 31.565        | 0.63  | 20000 | 30.6092         | 0.9869   | 0.4201    | 0.8975 | 0.5723 | 0.0131  |
+| 31.105        | 0.79  | 25000 | 30.2502         | 0.9871   | 0.4247    | 0.8998 | 0.5770 | 0.0129  |
+
+
+### Framework versions
+
+- Transformers 4.35.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.7.1
+- Tokenizers 0.14.1

all_results.json

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+    "train_loss": 37.88160453125,
+    "train_runtime": 37333.8115,
+    "train_samples_per_second": 42.857,
+    "train_steps_per_second": 0.67
+}

config.json

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+  ],
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special_tokens_map.json

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tokenizer_config.json

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train_results.json

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